Point cloud completion is a generation and estimation issue derived from the partial point
clouds, which plays a vital role in the applications of 3D computer vision. The progress of …

The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of
deep learning (DL). However, the latter faces various issues, including the lack of data or …

Point cloud completion aims to predict a complete shape in high accuracy from its partial
observation. However, previous methods usually suffered from discrete nature of point cloud …

Chamfer Distance (CD) and Earth Mover's Distance (EMD) are two broadly adopted metrics
for measuring the similarity between two point sets. However, CD is usually insensitive to …

Point cloud completion aims at completing geometric and topological shapes from a partial
observation. However, some topology of the original shape is missing, existing methods …

While previous studies have demonstrated successful 3D object shape completion with a
sufficient number of points they often fail in scenarios when a few points eg tens of points are …

Most existing point cloud completion methods suffer from the discrete nature of point clouds
and the unstructured prediction of points in local regions, which makes it difficult to reveal …

Point cloud sampling is crucial for efficient large-scale point cloud analysis, where learning-
to-sample methods have recently received increasing attention from the community for …

Deep learning technique has yielded significant improvements in point cloud completion
with the aim of completing missing object shapes from partial inputs. However, most existing …

Abstract Chamfer Distance (CD) and Earth Mover's Distance (EMD) are two broadly adopted
metrics for measuring the similarity between two point sets. However, CD is usually …